Automated Semantic Flattening: How Large Language Models Threaten the Cognitive Architecture of Arabic beyond Digital Extinction

Abstract

While existing literature on digital linguistics frequently warns against "Digital Language
Death"—framed as a quantitative scarcity of Arabic content online—this study argues
that the primary threat to the Arabic language in the era of Artificial Intelligence is not
extinction, but qualitative semantic degradation. We introduce the concept of
"Automated Semantic Flattening" (ASF), defining it as the algorithmic erosion of the rich,
multi-layered derivational root system (Ishtiqāq) unique to the Arabic language when
processed through Large Language Models (LLMs). Built primarily on Indo-European
structural and computational logics, transformer-based architectures tokenize Arabic
texts in a manner that strips lexical roots of their deep metaphoric, spatial, and
philosophical nuances. Through a qualitative comparative methodology analyzing
algorithmic outputs against traditional lexicographical frameworks, this paper
demonstrates how LLMs favor surface-level, unidimensional equivalents, thereby
reconstructing Arabic into a structural shell for Western semantic frameworks. The
study concludes by proposing a framework for "Digital Linguistic Sovereignty,"
advocating for AI architectures natively designed around the root-and-pattern
morphology rather than mere computational translation.